Genstruct 7B is an instruction-generation model, designed to create valid instructions given a raw text corpus. This enables the creation of new, partially synthetic instruction finetuning datasets from any raw-text corpus.
This work was inspired by
Ada-InstructPrevious methods largely rely on in-context approaches to generate instructions, while Ada-Instruct trained a custom instruction-generation model.
Inspired by this, we took this approach further by grounding the generations in user-provided context passages.
Further, the model is trained to generate questions involving complex scenarios that require detailed reasoning, allowing for models trained on the generated data to reason step-by-step.
1from awq import AutoAWQForCausalLM
2from transformers import AutoTokenizer, TextStreamer
3
4model_path = "solidrust/Genstruct-7B-AWQ"
5system_message = "You are Genstruct, incarnated as a powerful AI."
6
7# Load model
8model = AutoAWQForCausalLM.from_quantized(model_path,
9 fuse_layers=True)
10tokenizer = AutoTokenizer.from_pretrained(model_path,
11 trust_remote_code=True)
12streamer = TextStreamer(tokenizer,
13 skip_prompt=True,
14 skip_special_tokens=True)
15
16# Convert prompt to tokens
17prompt_template = """\
18<|im_start|>system
19{system_message}<|im_end|>
20<|im_start|>user
21{prompt}<|im_end|>
22<|im_start|>assistant"""
23
24prompt = "You're standing on the surface of the Earth. "\
25 "You walk one mile south, one mile west and one mile north. "\
26 "You end up exactly where you started. Where are you?"
27
28tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
29 return_tensors='pt').input_ids.cuda()
30
31# Generate output
32generation_output = model.generate(tokens,
33 streamer=streamer,
34 max_new_tokens=512)
35
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
1<|im_start|>system
2{system_message}<|im_end|>
3<|im_start|>user
4{prompt}<|im_end|>
5<|im_start|>assistant
1@misc{Genstruct,
2 url={[https://https://huggingface.co/NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/https://huggingface.co/NousResearch/Genstruct-7B)},
3 title={Genstruct},
4 author={"euclaise"}
5}
